Virtual Simulated Placements in Health Care Education: Scoping Review
Bibliographic record
Abstract
Background: A virtual simulated placement (VSP) is a computer-based version of a practice placement. COVID-19 drove increased adoption of web-based technology in clinical education. Accordingly, the number of VSP publications increased from 2020. This review determines the scope of this literature to inform future research questions. Objective: This study aimed to assess the range and types of evidence related to VSPs across the health care professions. Methods: Studies that focussed on health care students participating in VSPs. Hybrid, augmented reality, and mixed reality placements were excluded. In total, 14 databases were searched, limited to English, and dated from January 1, 2020. Supplementary searches were employed, and an updated search was conducted on July 9, 2023. Themes were synthesized using the PAGER (patterns, advances, gaps, evidence for practice, and research recommendations) framework to highlight patterns, advances, gaps, evidence for practice, and research recommendations. Results: In total, 28 papers were reviewed. All VSPs were designed in response to pandemic restrictions. Students were primarily from medicine and nursing. Few publications were from low and middle-income countries. There was limited stakeholder involvement in the VSP designs and a lack of robust research designs, consistent outcome measures, conceptual underpinnings, and immersive technologies. Despite this, promising trends for student experience, knowledge, communication, and critical thinking skills using VSPs have emerged. Conclusions: This review maps the VSP evidence across health care education. Allied health and midwifery research require greater representation, and based on the highlighted gaps, other areas for future research are suggested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".